文件列表:

data/ (0, 2018-05-07)
data/dataset09.mat (2613235, 2018-05-07)
main.m (8592, 2018-05-07)
plots/ (0, 2018-05-07)
plots/plot1.svg (49731, 2018-05-07)
plots/plot10.svg (32855, 2018-05-07)
plots/plot11.svg (50283, 2018-05-07)
plots/plot2.svg (417855, 2018-05-07)
plots/plot3.svg (27410, 2018-05-07)
plots/plot4.svg (39798, 2018-05-07)
plots/plot5.svg (29903, 2018-05-07)
plots/plot6.svg (27539, 2018-05-07)
plots/plot7.svg (29474, 2018-05-07)
plots/plot8.svg (71305, 2018-05-07)
plots/plot9.svg (41488, 2018-05-07)

# eeg-power_variability This project concerns the combined study of two signals: EEG power variability (EEG-PV) and middle cerebral artery blood flow velocity (BFV). A first phase of the study was conducted in subjects under anaesthesia, so in a non-physiological situation where heartbeat and breathing oscillations are limited. The results were published in (*Zanatta et al., The human brain pacemaker, Neuroimage 2013*). The study is now extended to subjects studied under normal conditions. The dataset used was provided in the Biological Signals Processing course, held by Professor Toffolo, Bioengineering, Department of Information Engineering, University of Padua, academic year 2014/2015. The code is contained in the scripit `main.m`. The two signals provided in the `data` folder are: `BFV2` : blood flow rate (cm/sec) measured with ecodoppler at the right cerebral artery with sampling frequency Fs BFV = 0.5 Hz. `F4C4` : EEG (mV) measured from a frontal derivation located on the right side of the scalp with sampling frequency Fs EEG = 512 Hz. To extract the delta component (`DELTA2`: 0-4Hz) from `F4C4` a **lowpass filter** was used, the optimum order of 5 was found with the `ellipord` function while the `b` and `a` parameters were obtained thanks to the `ellip` function. To eliminate phase distortion, Forward-Backward filtering was used using the `filtfilt` function. The **Difference equation** of the 5th order filter is: ```y(n) = 4.9382y(n-1)-9.7584y(n-2) + 9.6455y(n-3)-4.7689y(n-4) + 0.9435y(n-5) + 0.0044x(n)-0.0131x(n-1) + 0.0087x(n-2) + 0.0087x(n-3)-0.0131x(n-4) + 0.0044x(n-5)``` *Gain* and *Phase* of the **Frequency Response** of the filter are shown in Fig. 1:  **Figure 1:** Frequency Response of the lowpass filter The result of the filtering process is `DELTA2`, shown in Fig 2.:  **Figure 2:** Signals `F4C4` and `DELTA2` with mean and SD intervals Once the delta component (`DELTA2` signal) had been obtained through filtering, it was segmented into 150 segments by 1024 samples at 2-second intervals. To construct the **Power Variability** signal (`DELTA2_PV`), the spectrum of each segment was calculated using the **Periodogram** method, having first removed the average for each segment. Then each spectrum was integrated, with `trapz`, to obtain the power relative to the delta band. `DELTA2_PV` was formed by combining the values for each segment. The obtained signal `DELTA2_PV` is shown in Fig. 3:  **Figure 3:** Power Variability in the delta band The spectrum of `DELTA2_PV` signal, obtained by the Periodogram method is shown in Fig 4:  **Figure 4:** Spectrum of `DELTA2_PV` Subsequently the **AR model** of optimal order was identified on the `DELTA2_PV` signal. To find the optimal, three indicators were evaluated for every order between [1:20]: **MSE**, **Akaike's Final Prediction Error (FPE)**, **Akaike Information Criterion (AIC)**. AIC and FPE returned 1 as optimal order while MSE returned 3. The results for MSE, AIC and FPE are shown in Fig. 5, Fig. 6, Fig. 7:  **Figure 5:** MSE for 20 model orders  **Figure 6:** AIC for 20 model orders  **Figure 7:** FPE for 20 model orders **Anderson–Darling test** was carried out to verify the whiteness of the prediction error and the level of significance was set at α = 5%. The results of Anderson test are show in Fig. 8:  **Figure 8:** Anderson–Darling test, with α = 5% Accordingly, by adopting order 1, the **difference equation** of the **AR** model is: ```y(n)= -0.0664*y(n-1) + x(n)``` Then, the optimal order AR model is used to estimate the spectrum of DELTA2 PV signal. The model cannot explain the data properly and seems to do oversmoothing. The **Power Spectral Density** obtained in this way is shown in Fig. 9:  **Figure 9:** Power Spectral Density obtained from the AR model As a consequence of what has just been said, the **Spectral Coherence** is calculated between `DELTA2_PV` obtained by the Periodogram's method and `BFV2`. The `mscohere` function was used to calculate the Coherence function between `DELTA2_PV` and `BFV2`. After a tuning phase, a window containing 30 samples (L/5) was used, where `L = 150` is the number of `DELTA2_PV` and `BFV2` samples. As overlap a number of samples equal to 50% of the window was used. The Spectral Coherence has a maximum of `max = 0.5911` at a frequency of 0.095 Hz indicating the presence of a possible casuality link between `DELTA2_PV` and `BFV2`. Coherence function is shown in Fig. 10:  **Figure 10:** Spectral Coherence between `DELTA2_PV` and `BFV2`

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：